Instructions to use moonshotai/Kimi-VL-A3B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use moonshotai/Kimi-VL-A3B-Instruct with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="moonshotai/Kimi-VL-A3B-Instruct", trust_remote_code=True)
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("moonshotai/Kimi-VL-A3B-Instruct", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use moonshotai/Kimi-VL-A3B-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "moonshotai/Kimi-VL-A3B-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "moonshotai/Kimi-VL-A3B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/moonshotai/Kimi-VL-A3B-Instruct

SGLang

How to use moonshotai/Kimi-VL-A3B-Instruct with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "moonshotai/Kimi-VL-A3B-Instruct" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "moonshotai/Kimi-VL-A3B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "moonshotai/Kimi-VL-A3B-Instruct" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "moonshotai/Kimi-VL-A3B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use moonshotai/Kimi-VL-A3B-Instruct with Docker Model Runner:
```
docker model run hf.co/moonshotai/Kimi-VL-A3B-Instruct
```

zhouzaida commited on Apr 20, 2025

Commit

bd66f6c

verified ·

1 Parent(s): a2143f9

remove log from tokenizer (#18)

Browse files

- remove log from tokenizer (704cff53c1103702626e95d61c41bf608fea096d)

Files changed (1) hide show

tokenization_moonshot.py +0 -10

tokenization_moonshot.py CHANGED Viewed

@@ -94,12 +94,6 @@ class TikTokenTokenizer(PreTrainedTokenizer):
             i: added_tokens_decoder[i].content for i in added_tokens_decoder
         }
-        special_tokens = (
-            [str(bos_token), str(eos_token)]
-            + additional_special_tokens
-            + [str(unk_token), str(pad_token)]
-        )
         self.vocab_file = vocab_file
         mergeable_ranks = load_tiktoken_bpe(vocab_file)
         num_base_tokens = len(mergeable_ranks)
@@ -116,15 +110,11 @@ class TikTokenTokenizer(PreTrainedTokenizer):
             mergeable_ranks=mergeable_ranks,
             special_tokens=self.special_tokens,
         )
-        logger.info(f"Reloaded tiktoken model from {vocab_file}")
         self.n_words: int = self.model.n_vocab
         # BOS / EOS token IDs
         self.bos_id: int = self.special_tokens[str(bos_token)]
         self.eos_id: int = self.special_tokens[str(eos_token)]
-        logger.info(
-            f"#words: {self.n_words} - BOS ID: {self.bos_id} - EOS ID: {self.eos_id}"
-        )
         self.pad_id: int = self.special_tokens[str(pad_token)]
         self.unk_id: int = self.special_tokens[str(unk_token)]

             i: added_tokens_decoder[i].content for i in added_tokens_decoder
         }
         self.vocab_file = vocab_file
         mergeable_ranks = load_tiktoken_bpe(vocab_file)
         num_base_tokens = len(mergeable_ranks)
             mergeable_ranks=mergeable_ranks,
             special_tokens=self.special_tokens,
         )
         self.n_words: int = self.model.n_vocab
         # BOS / EOS token IDs
         self.bos_id: int = self.special_tokens[str(bos_token)]
         self.eos_id: int = self.special_tokens[str(eos_token)]
         self.pad_id: int = self.special_tokens[str(pad_token)]
         self.unk_id: int = self.special_tokens[str(unk_token)]